recent publication from the Donnelly group comparing using GENCODE or Refeq annotation when predicting variants effects.
Choice of transcripts and software has a large effect on variant annotation
Genome Medicine 2014, 6:26 doi:10.1186/gm543
Original article emphasizing the importance of networks to cancer
http://www.nature.com/nature/journal/v408/n6810/full/408307a0.html
A paper that was done by Vogelstein, Lane and Levine in Nature (2000, November 16) that talks about how cancer is associated with a network and these network of genes associated with p53. This is in response to the idea that p53 is such a crucial molecule in cancer as a tumor suppressor and it marks well known cancer biologists discussing this from a network perspective.
QT:"We show that many translocation-prone pairs of regions genome-wide, including the cancer translocation partners BCR-ABL and MYC-IGH, display elevated Hi-C contact frequencies in normal human cells."
Overlap of Hi-C & gene fusions
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0044196
Developmental dynamics… of random monoallelic gene expression. #RNAseq on 100s of instances without causative SNPs
An analysis of differentiated cell lines.
http://www.cell.com/developmental-cell/abstract/S1534-5807(14)00057-4
Interesting study looking at the effect of gene duplication on TF binding
"…when a TF is shared among many binding sites, either due to multiple identical copies of a gene regulated by that TF or due to unrelated genes that also independently bind the TF, the correlation in occupancy between the binding sites will lead to a complex dosage response to that TF." http://www.cell.com/abstract/S0092-8674(14)00221-9
http://genome.cshlp.org/content/early/2014/03/11/gr.164822.113.abstract
“We further demonstrate that the observed signatures of positive selection correlate better with the presence of regulatory sequences, as predicted by the ENCODE Project Consortium, than with the positions of amino acid substitutions. Our results suggest that adaptation was frequent in human evolution and provide support for the hypothesis of King and Wilson that adaptive divergence is primarily driven by regulatory changes.”
Similar to conclusion positive-section section in FunSeq paper
Finding lost treasures in #exome sequencing data. Mining off-target, often noncoding, reads from 1000G, TCGA, ESP, &c
http://www.ncbi.nlm.nih.gov/pubmed/23972387
paper from RECOMB 2014 might be interesting to you, which uses
hitting time in random walk to identify cancer drivers.
http://link.springer.com/chapter/10.1007%2F978-3-319-05269-4_23
HIT.nDRIVE: Multi-Driver Gene Prioritization based on Hitting Time
Raunak Shrestha, Ermin Hodzic, Jake Yeung, Kendric Wang, Thomas
Sauerwald, Phuong Dao, Shawn Anderson, Himisha Beltran, Mark A. Rubin,
Colin Collins, Gholamreza Haffari and S. Cenk Sahinalp.